Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations102814
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.4 MiB
Average record size in memory708.0 B

Variable types

Numeric7
Text8
Categorical3
Boolean2

Alerts

tournament_status has constant value "Professional" Constant
best_of is highly overall correlated with player1_score and 1 other fieldsHigh correlation
is_international_tournament is highly overall correlated with tournament_countryHigh correlation
match_id is highly overall correlated with season and 1 other fieldsHigh correlation
player1_score is highly overall correlated with best_of and 2 other fieldsHigh correlation
player2_score is highly overall correlated with player1_scoreHigh correlation
season is highly overall correlated with match_id and 2 other fieldsHigh correlation
tournament_country is highly overall correlated with is_international_tournamentHigh correlation
tournament_id is highly overall correlated with match_id and 1 other fieldsHigh correlation
tournament_prize_pool is highly overall correlated with best_of and 1 other fieldsHigh correlation
year is highly overall correlated with seasonHigh correlation
match_id has unique values Unique
player1_score has 3466 (3.4%) zeros Zeros
player2_score has 19309 (18.8%) zeros Zeros
tournament_prize_pool has 5473 (5.3%) zeros Zeros

Reproduction

Analysis started2025-08-27 12:26:18.677059
Analysis finished2025-08-27 12:26:23.037404
Duration4.36 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

tournament_id
Real number (ℝ)

High correlation 

Distinct919
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean747.03573
Minimum1
Maximum3363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2025-08-27T13:26:23.072794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73
Q1307
median457
Q3789
95-th percentile2691
Maximum3363
Range3362
Interquartile range (IQR)482

Descriptive statistics

Standard deviation782.76356
Coefficient of variation (CV)1.0478261
Kurtosis2.2181582
Mean747.03573
Median Absolute Deviation (MAD)194
Skewness1.825002
Sum76805732
Variance612718.79
MonotonicityNot monotonic
2025-08-27T13:26:23.126786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
475 588
 
0.6%
481 578
 
0.6%
458 564
 
0.5%
473 559
 
0.5%
449 556
 
0.5%
460 553
 
0.5%
482 547
 
0.5%
468 541
 
0.5%
464 541
 
0.5%
452 534
 
0.5%
Other values (909) 97253
94.6%
ValueCountFrequency (%)
1 101
0.1%
2 205
0.2%
3 108
0.1%
12 192
0.2%
13 23
 
< 0.1%
15 95
0.1%
16 102
0.1%
17 15
 
< 0.1%
18 95
0.1%
20 126
0.1%
ValueCountFrequency (%)
3363 125
0.1%
3320 127
0.1%
3299 128
0.1%
3284 15
 
< 0.1%
3275 96
0.1%
3271 128
0.1%
3270 1
 
< 0.1%
3267 127
0.1%
3188 23
 
< 0.1%
3186 63
0.1%

match_id
Real number (ℝ)

High correlation  Unique 

Distinct102814
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66542.816
Minimum1
Maximum208001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2025-08-27T13:26:23.180969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7022.65
Q129000.25
median56443.5
Q384029.75
95-th percentile174628.35
Maximum208001
Range208000
Interquartile range (IQR)55029.5

Descriptive statistics

Standard deviation49941.182
Coefficient of variation (CV)0.750512
Kurtosis0.36638656
Mean66542.816
Median Absolute Deviation (MAD)27515
Skewness1.0451854
Sum6.8415331 × 109
Variance2.4941217 × 109
MonotonicityNot monotonic
2025-08-27T13:26:23.231309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82716 1
 
< 0.1%
1545 1
 
< 0.1%
1608 1
 
< 0.1%
1557 1
 
< 0.1%
1565 1
 
< 0.1%
1607 1
 
< 0.1%
1597 1
 
< 0.1%
1591 1
 
< 0.1%
1602 1
 
< 0.1%
1573 1
 
< 0.1%
Other values (102804) 102804
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
208001 1
< 0.1%
208000 1
< 0.1%
207999 1
< 0.1%
207998 1
< 0.1%
207997 1
< 0.1%
207996 1
< 0.1%
207995 1
< 0.1%
207994 1
< 0.1%
207993 1
< 0.1%
207992 1
< 0.1%

stage
Text

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
2025-08-27T13:26:23.306932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length26
Median length7
Mean length7.9806933
Min length5

Characters and Unicode

Total characters820527
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinal
2nd rowSemi-final
3rd rowSemi-final
4th rowQuarter-final
5th rowQuarter-final
ValueCountFrequency (%)
last 59550
30.0%
round 26784
13.5%
128 19081
 
9.6%
64 13986
 
7.0%
1 9871
 
5.0%
32 9420
 
4.7%
2 8701
 
4.4%
96 6673
 
3.4%
16 5567
 
2.8%
3 5444
 
2.7%
Other values (24) 33729
17.0%
2025-08-27T13:26:23.450047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95992
 
11.7%
a 75630
 
9.2%
t 62962
 
7.7%
s 59994
 
7.3%
L 59550
 
7.3%
u 40926
 
5.0%
n 39498
 
4.8%
2 37234
 
4.5%
1 35095
 
4.3%
o 31656
 
3.9%
Other values (30) 281990
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 820527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
95992
 
11.7%
a 75630
 
9.2%
t 62962
 
7.7%
s 59994
 
7.3%
L 59550
 
7.3%
u 40926
 
5.0%
n 39498
 
4.8%
2 37234
 
4.5%
1 35095
 
4.3%
o 31656
 
3.9%
Other values (30) 281990
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 820527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
95992
 
11.7%
a 75630
 
9.2%
t 62962
 
7.7%
s 59994
 
7.3%
L 59550
 
7.3%
u 40926
 
5.0%
n 39498
 
4.8%
2 37234
 
4.5%
1 35095
 
4.3%
o 31656
 
3.9%
Other values (30) 281990
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 820527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
95992
 
11.7%
a 75630
 
9.2%
t 62962
 
7.7%
s 59994
 
7.3%
L 59550
 
7.3%
u 40926
 
5.0%
n 39498
 
4.8%
2 37234
 
4.5%
1 35095
 
4.3%
o 31656
 
3.9%
Other values (30) 281990
34.4%

best_of
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0719844
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2025-08-27T13:26:23.511530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q17
median9
Q39
95-th percentile19
Maximum35
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.3365292
Coefficient of variation (CV)0.36778384
Kurtosis7.8320675
Mean9.0719844
Median Absolute Deviation (MAD)0
Skewness2.2371419
Sum932727
Variance11.132427
MonotonicityNot monotonic
2025-08-27T13:26:23.554846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
9 54884
53.4%
7 28557
27.8%
11 5498
 
5.3%
19 4680
 
4.6%
5 3239
 
3.2%
17 2893
 
2.8%
1 1007
 
1.0%
3 792
 
0.8%
25 465
 
0.5%
4 336
 
0.3%
Other values (10) 463
 
0.5%
ValueCountFrequency (%)
1 1007
 
1.0%
2 44
 
< 0.1%
3 792
 
0.8%
4 336
 
0.3%
5 3239
 
3.2%
6 40
 
< 0.1%
7 28557
27.8%
8 63
 
0.1%
9 54884
53.4%
11 5498
 
5.3%
ValueCountFrequency (%)
35 41
 
< 0.1%
33 46
 
< 0.1%
31 43
 
< 0.1%
25 465
 
0.5%
23 6
 
< 0.1%
21 7
 
< 0.1%
19 4680
4.6%
17 2893
2.8%
15 47
 
< 0.1%
13 126
 
0.1%
Distinct2308
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2025-08-27T13:26:23.758701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length29
Median length25
Mean length12.256804
Min length4

Characters and Unicode

Total characters1260171
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique442 ?
Unique (%)0.4%

Sample

1st rowTerry Griffiths
2nd rowTerry Griffiths
3rd rowAlex Higgins
4th rowTerry Griffiths
5th rowAlex Higgins
ValueCountFrequency (%)
mark 5335
 
2.6%
john 3255
 
1.6%
stephen 2859
 
1.4%
david 2605
 
1.3%
paul 2412
 
1.2%
steve 2341
 
1.1%
joe 2277
 
1.1%
michael 2236
 
1.1%
stuart 1687
 
0.8%
matthew 1650
 
0.8%
Other values (2556) 179771
87.1%
2025-08-27T13:26:24.031449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 108539
 
8.6%
a 103782
 
8.2%
103614
 
8.2%
n 98077
 
7.8%
i 79180
 
6.3%
r 76478
 
6.1%
o 66906
 
5.3%
l 60194
 
4.8%
t 48381
 
3.8%
s 39011
 
3.1%
Other values (58) 476009
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1260171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 108539
 
8.6%
a 103782
 
8.2%
103614
 
8.2%
n 98077
 
7.8%
i 79180
 
6.3%
r 76478
 
6.1%
o 66906
 
5.3%
l 60194
 
4.8%
t 48381
 
3.8%
s 39011
 
3.1%
Other values (58) 476009
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1260171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 108539
 
8.6%
a 103782
 
8.2%
103614
 
8.2%
n 98077
 
7.8%
i 79180
 
6.3%
r 76478
 
6.1%
o 66906
 
5.3%
l 60194
 
4.8%
t 48381
 
3.8%
s 39011
 
3.1%
Other values (58) 476009
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1260171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 108539
 
8.6%
a 103782
 
8.2%
103614
 
8.2%
n 98077
 
7.8%
i 79180
 
6.3%
r 76478
 
6.1%
o 66906
 
5.3%
l 60194
 
4.8%
t 48381
 
3.8%
s 39011
 
3.1%
Other values (58) 476009
37.8%
Distinct3527
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2025-08-27T13:26:24.247525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length29
Median length26
Mean length12.341918
Min length4

Characters and Unicode

Total characters1268922
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique941 ?
Unique (%)0.9%

Sample

1st rowAlex Higgins
2nd rowTony Meo
3rd rowRay Reardon
4th rowSteve Davis
5th rowJohn Spencer
ValueCountFrequency (%)
mark 4048
 
2.0%
john 2857
 
1.4%
david 2781
 
1.3%
paul 2468
 
1.2%
steve 2181
 
1.1%
joe 2051
 
1.0%
michael 2042
 
1.0%
stephen 2009
 
1.0%
ian 1615
 
0.8%
tony 1606
 
0.8%
Other values (3832) 183424
88.6%
2025-08-27T13:26:24.522080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 110195
 
8.7%
a 108138
 
8.5%
104268
 
8.2%
n 97106
 
7.7%
i 78287
 
6.2%
r 77534
 
6.1%
o 68694
 
5.4%
l 59938
 
4.7%
t 47366
 
3.7%
s 41137
 
3.2%
Other values (62) 476259
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1268922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 110195
 
8.7%
a 108138
 
8.5%
104268
 
8.2%
n 97106
 
7.7%
i 78287
 
6.2%
r 77534
 
6.1%
o 68694
 
5.4%
l 59938
 
4.7%
t 47366
 
3.7%
s 41137
 
3.2%
Other values (62) 476259
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1268922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 110195
 
8.7%
a 108138
 
8.5%
104268
 
8.2%
n 97106
 
7.7%
i 78287
 
6.2%
r 77534
 
6.1%
o 68694
 
5.4%
l 59938
 
4.7%
t 47366
 
3.7%
s 41137
 
3.2%
Other values (62) 476259
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1268922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 110195
 
8.7%
a 108138
 
8.5%
104268
 
8.2%
n 97106
 
7.7%
i 78287
 
6.2%
r 77534
 
6.1%
o 68694
 
5.4%
l 59938
 
4.7%
t 47366
 
3.7%
s 41137
 
3.2%
Other values (62) 476259
37.5%

player1_score
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8829829
Minimum0
Maximum18
Zeros3466
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2025-08-27T13:26:24.590246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q35
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8873861
Coefficient of variation (CV)0.38652318
Kurtosis5.6024033
Mean4.8829829
Median Absolute Deviation (MAD)0
Skewness1.1399234
Sum502039
Variance3.5622262
MonotonicityNot monotonic
2025-08-27T13:26:24.632048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
5 53463
52.0%
4 26954
26.2%
6 5428
 
5.3%
10 4607
 
4.5%
0 3466
 
3.4%
3 3323
 
3.2%
9 2859
 
2.8%
1 1006
 
1.0%
2 918
 
0.9%
13 465
 
0.5%
Other values (7) 325
 
0.3%
ValueCountFrequency (%)
0 3466
 
3.4%
1 1006
 
1.0%
2 918
 
0.9%
3 3323
 
3.2%
4 26954
26.2%
5 53463
52.0%
6 5428
 
5.3%
7 130
 
0.1%
8 52
 
0.1%
9 2859
 
2.8%
ValueCountFrequency (%)
18 41
 
< 0.1%
17 46
 
< 0.1%
16 43
 
< 0.1%
13 465
 
0.5%
12 6
 
< 0.1%
11 7
 
< 0.1%
10 4607
4.5%
9 2859
2.8%
8 52
 
0.1%
7 130
 
0.1%

player2_score
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1794892
Minimum0
Maximum17
Zeros19309
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2025-08-27T13:26:24.672591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8054482
Coefficient of variation (CV)0.82838137
Kurtosis3.588739
Mean2.1794892
Median Absolute Deviation (MAD)1
Skewness1.3161434
Sum224082
Variance3.2596433
MonotonicityNot monotonic
2025-08-27T13:26:24.714955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 22316
21.7%
1 20945
20.4%
3 20292
19.7%
0 19309
18.8%
4 13473
13.1%
5 2082
 
2.0%
7 1190
 
1.2%
6 1167
 
1.1%
8 1111
 
1.1%
9 674
 
0.7%
Other values (8) 255
 
0.2%
ValueCountFrequency (%)
0 19309
18.8%
1 20945
20.4%
2 22316
21.7%
3 20292
19.7%
4 13473
13.1%
5 2082
 
2.0%
6 1167
 
1.1%
7 1190
 
1.2%
8 1111
 
1.1%
9 674
 
0.7%
ValueCountFrequency (%)
17 3
 
< 0.1%
16 11
 
< 0.1%
15 19
 
< 0.1%
14 17
 
< 0.1%
13 9
 
< 0.1%
12 69
 
0.1%
11 76
 
0.1%
10 51
 
< 0.1%
9 674
0.7%
8 1111
1.1%

season
Categorical

High correlation 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
1992-1993
 
6360
1993-1994
 
5434
1991-1992
 
5191
1994-1995
 
5018
1995-1996
 
4826
Other values (34)
75985 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters925326
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1982-1983
2nd row1982-1983
3rd row1982-1983
4th row1982-1983
5th row1982-1983

Common Values

ValueCountFrequency (%)
1992-1993 6360
 
6.2%
1993-1994 5434
 
5.3%
1991-1992 5191
 
5.0%
1994-1995 5018
 
4.9%
1995-1996 4826
 
4.7%
1996-1997 4541
 
4.4%
2013-2014 4439
 
4.3%
2011-2012 3856
 
3.8%
2012-2013 3849
 
3.7%
2014-2015 3716
 
3.6%
Other values (29) 55584
54.1%

Length

2025-08-27T13:26:24.759001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1992-1993 6360
 
6.2%
1993-1994 5434
 
5.3%
1991-1992 5191
 
5.0%
1994-1995 5018
 
4.9%
1995-1996 4826
 
4.7%
1996-1997 4541
 
4.4%
2013-2014 4439
 
4.3%
2011-2012 3856
 
3.8%
2012-2013 3849
 
3.7%
2014-2015 3716
 
3.6%
Other values (29) 55584
54.1%

Most occurring characters

ValueCountFrequency (%)
9 193224
20.9%
1 182375
19.7%
0 163192
17.6%
2 135911
14.7%
- 102814
11.1%
8 35156
 
3.8%
3 25073
 
2.7%
4 23404
 
2.5%
5 22064
 
2.4%
6 21723
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 925326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 193224
20.9%
1 182375
19.7%
0 163192
17.6%
2 135911
14.7%
- 102814
11.1%
8 35156
 
3.8%
3 25073
 
2.7%
4 23404
 
2.5%
5 22064
 
2.4%
6 21723
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 925326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 193224
20.9%
1 182375
19.7%
0 163192
17.6%
2 135911
14.7%
- 102814
11.1%
8 35156
 
3.8%
3 25073
 
2.7%
4 23404
 
2.5%
5 22064
 
2.4%
6 21723
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 925326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 193224
20.9%
1 182375
19.7%
0 163192
17.6%
2 135911
14.7%
- 102814
11.1%
8 35156
 
3.8%
3 25073
 
2.7%
4 23404
 
2.5%
5 22064
 
2.4%
6 21723
 
2.3%

year
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.5344
Minimum1982
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2025-08-27T13:26:24.802141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile1988
Q11994
median2001
Q32012
95-th percentile2018
Maximum2020
Range38
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.07114
Coefficient of variation (CV)0.0050291968
Kurtosis-1.2889615
Mean2002.5344
Median Absolute Deviation (MAD)9
Skewness0.05511994
Sum2.0588857 × 108
Variance101.42785
MonotonicityIncreasing
2025-08-27T13:26:24.859341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1993 6295
 
6.1%
1992 5789
 
5.6%
1994 5421
 
5.3%
1995 4747
 
4.6%
1996 4695
 
4.6%
2013 4065
 
4.0%
2012 3821
 
3.7%
1997 3809
 
3.7%
2011 3662
 
3.6%
2015 3650
 
3.6%
Other values (29) 56860
55.3%
ValueCountFrequency (%)
1982 483
 
0.5%
1983 526
 
0.5%
1984 764
 
0.7%
1985 1137
1.1%
1986 1025
1.0%
1987 1055
1.0%
1988 1443
1.4%
1989 1739
1.7%
1990 1387
1.3%
1991 2551
2.5%
ValueCountFrequency (%)
2020 96
 
0.1%
2019 3000
2.9%
2018 3172
3.1%
2017 3285
3.2%
2016 3278
3.2%
2015 3650
3.6%
2014 3600
3.5%
2013 4065
4.0%
2012 3821
3.7%
2011 3662
3.6%
Distinct219
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
2025-08-27T13:26:25.050839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length40
Median length36
Mean length16.405567
Min length6

Characters and Unicode

Total characters1686722
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUK Championship
2nd rowUK Championship
3rd rowUK Championship
4th rowUK Championship
5th rowUK Championship
ValueCountFrequency (%)
open 29338
 
10.1%
event 27301
 
9.4%
27025
 
9.3%
championship 24113
 
8.3%
world 11057
 
3.8%
tour 10091
 
3.5%
european 9138
 
3.1%
uk 7960
 
2.7%
masters 6877
 
2.4%
1 6235
 
2.1%
Other values (141) 131174
45.2%
2025-08-27T13:26:25.322766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
187495
 
11.1%
n 150167
 
8.9%
e 119285
 
7.1%
a 106449
 
6.3%
i 101374
 
6.0%
p 88335
 
5.2%
o 84540
 
5.0%
h 83416
 
4.9%
s 80614
 
4.8%
r 76346
 
4.5%
Other values (51) 608701
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1686722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
187495
 
11.1%
n 150167
 
8.9%
e 119285
 
7.1%
a 106449
 
6.3%
i 101374
 
6.0%
p 88335
 
5.2%
o 84540
 
5.0%
h 83416
 
4.9%
s 80614
 
4.8%
r 76346
 
4.5%
Other values (51) 608701
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1686722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
187495
 
11.1%
n 150167
 
8.9%
e 119285
 
7.1%
a 106449
 
6.3%
i 101374
 
6.0%
p 88335
 
5.2%
o 84540
 
5.0%
h 83416
 
4.9%
s 80614
 
4.8%
r 76346
 
4.5%
Other values (51) 608701
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1686722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
187495
 
11.1%
n 150167
 
8.9%
e 119285
 
7.1%
a 106449
 
6.3%
i 101374
 
6.0%
p 88335
 
5.2%
o 84540
 
5.0%
h 83416
 
4.9%
s 80614
 
4.8%
r 76346
 
4.5%
Other values (51) 608701
36.1%

tournament_country
Categorical

High correlation 

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
England
50808 
Wales
12232 
China
7891 
Thailand
6041 
Germany
5214 
Other values (31)
20628 

Length

Max length20
Median length7
Mean length7.0730153
Min length5

Characters and Unicode

Total characters727205
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngland
2nd rowEngland
3rd rowEngland
4th rowEngland
5th rowEngland

Common Values

ValueCountFrequency (%)
England 50808
49.4%
Wales 12232
 
11.9%
China 7891
 
7.7%
Thailand 6041
 
5.9%
Germany 5214
 
5.1%
Scotland 4852
 
4.7%
Belgium 3241
 
3.2%
United Arab Emirates 2238
 
2.2%
Malta 1705
 
1.7%
Ireland 1190
 
1.2%
Other values (26) 7402
 
7.2%

Length

2025-08-27T13:26:25.399027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
england 50808
46.8%
wales 12232
 
11.3%
china 7891
 
7.3%
thailand 6041
 
5.6%
germany 5214
 
4.8%
scotland 4852
 
4.5%
belgium 3241
 
3.0%
united 2238
 
2.1%
arab 2238
 
2.1%
emirates 2238
 
2.1%
Other values (34) 11644
 
10.7%

Most occurring characters

ValueCountFrequency (%)
n 134157
18.4%
a 113009
15.5%
l 84889
11.7%
d 68295
9.4%
g 55416
7.6%
E 53050
 
7.3%
e 29386
 
4.0%
i 25959
 
3.6%
r 17389
 
2.4%
s 15598
 
2.1%
Other values (34) 130057
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 727205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 134157
18.4%
a 113009
15.5%
l 84889
11.7%
d 68295
9.4%
g 55416
7.6%
E 53050
 
7.3%
e 29386
 
4.0%
i 25959
 
3.6%
r 17389
 
2.4%
s 15598
 
2.1%
Other values (34) 130057
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 727205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 134157
18.4%
a 113009
15.5%
l 84889
11.7%
d 68295
9.4%
g 55416
7.6%
E 53050
 
7.3%
e 29386
 
4.0%
i 25959
 
3.6%
r 17389
 
2.4%
s 15598
 
2.1%
Other values (34) 130057
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 727205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 134157
18.4%
a 113009
15.5%
l 84889
11.7%
d 68295
9.4%
g 55416
7.6%
E 53050
 
7.3%
e 29386
 
4.0%
i 25959
 
3.6%
r 17389
 
2.4%
s 15598
 
2.1%
Other values (34) 130057
17.9%

tournament_prize_pool
Real number (ℝ)

High correlation  Zeros 

Distinct554
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292159.97
Minimum0
Maximum2220000
Zeros5473
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size803.4 KiB
2025-08-27T13:26:25.449133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150000
median200500
Q3366000
95-th percentile1111000
Maximum2220000
Range2220000
Interquartile range (IQR)316000

Descriptive statistics

Standard deviation340591.31
Coefficient of variation (CV)1.1657699
Kurtosis6.0632986
Mean292159.97
Median Absolute Deviation (MAD)150500
Skewness2.2938879
Sum3.0038135 × 1010
Variance1.1600244 × 1011
MonotonicityNot monotonic
2025-08-27T13:26:25.499683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5473
 
5.3%
50000 3850
 
3.7%
104157 1353
 
1.3%
330000 1331
 
1.3%
93750 1320
 
1.3%
150000 1064
 
1.0%
400000 994
 
1.0%
13280 977
 
1.0%
325000 890
 
0.9%
120000 836
 
0.8%
Other values (544) 84726
82.4%
ValueCountFrequency (%)
0 5473
5.3%
500 15
 
< 0.1%
1380 26
 
< 0.1%
1600 214
 
0.2%
1700 15
 
< 0.1%
2475 81
 
0.1%
2700 7
 
< 0.1%
2850 11
 
< 0.1%
3000 40
 
< 0.1%
3100 8
 
< 0.1%
ValueCountFrequency (%)
2220000 143
 
0.1%
1978000 143
 
0.1%
1815250 299
0.3%
1751000 143
 
0.1%
1614585 323
0.3%
1532000 302
0.3%
1499100 143
 
0.1%
1456150 355
0.3%
1410150 424
0.4%
1379600 367
0.4%

tournament_status
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Professional
102814 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1233768
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProfessional
2nd rowProfessional
3rd rowProfessional
4th rowProfessional
5th rowProfessional

Common Values

ValueCountFrequency (%)
Professional 102814
100.0%

Length

2025-08-27T13:26:25.543991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-27T13:26:25.576429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
professional 102814
100.0%

Most occurring characters

ValueCountFrequency (%)
o 205628
16.7%
s 205628
16.7%
P 102814
8.3%
r 102814
8.3%
f 102814
8.3%
e 102814
8.3%
i 102814
8.3%
n 102814
8.3%
a 102814
8.3%
l 102814
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1233768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 205628
16.7%
s 205628
16.7%
P 102814
8.3%
r 102814
8.3%
f 102814
8.3%
e 102814
8.3%
i 102814
8.3%
n 102814
8.3%
a 102814
8.3%
l 102814
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1233768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 205628
16.7%
s 205628
16.7%
P 102814
8.3%
r 102814
8.3%
f 102814
8.3%
e 102814
8.3%
i 102814
8.3%
n 102814
8.3%
a 102814
8.3%
l 102814
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1233768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 205628
16.7%
s 205628
16.7%
P 102814
8.3%
r 102814
8.3%
f 102814
8.3%
e 102814
8.3%
i 102814
8.3%
n 102814
8.3%
a 102814
8.3%
l 102814
8.3%
Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-08-27T13:26:25.687605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length20
Median length7
Mean length7.2009551
Min length4

Characters and Unicode

Total characters740359
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowWales
2nd rowWales
3rd rowNorthern Ireland
4th rowWales
5th rowNorthern Ireland
ValueCountFrequency (%)
england 61399
57.0%
scotland 9182
 
8.5%
wales 8567
 
8.0%
ireland 7626
 
7.1%
china 4215
 
3.9%
northern 3409
 
3.2%
thailand 1870
 
1.7%
australia 1604
 
1.5%
canada 1215
 
1.1%
belgium 1107
 
1.0%
Other values (66) 7538
 
7.0%
2025-08-27T13:26:25.886495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 154955
20.9%
a 108233
14.6%
l 93825
12.7%
d 83219
11.2%
g 63877
8.6%
E 61471
 
8.3%
e 22679
 
3.1%
r 18557
 
2.5%
t 16256
 
2.2%
o 14998
 
2.0%
Other values (36) 102289
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 154955
20.9%
a 108233
14.6%
l 93825
12.7%
d 83219
11.2%
g 63877
8.6%
E 61471
 
8.3%
e 22679
 
3.1%
r 18557
 
2.5%
t 16256
 
2.2%
o 14998
 
2.0%
Other values (36) 102289
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 154955
20.9%
a 108233
14.6%
l 93825
12.7%
d 83219
11.2%
g 63877
8.6%
E 61471
 
8.3%
e 22679
 
3.1%
r 18557
 
2.5%
t 16256
 
2.2%
o 14998
 
2.0%
Other values (36) 102289
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 154955
20.9%
a 108233
14.6%
l 93825
12.7%
d 83219
11.2%
g 63877
8.6%
E 61471
 
8.3%
e 22679
 
3.1%
r 18557
 
2.5%
t 16256
 
2.2%
o 14998
 
2.0%
Other values (36) 102289
13.8%
Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-08-27T13:26:26.046229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length20
Median length7
Mean length7.1923863
Min length4

Characters and Unicode

Total characters739478
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowNorthern Ireland
2nd rowEngland
3rd rowWales
4th rowEngland
5th rowEngland
ValueCountFrequency (%)
england 61074
56.6%
wales 7526
 
7.0%
ireland 7470
 
6.9%
scotland 7450
 
6.9%
china 4374
 
4.1%
northern 3110
 
2.9%
thailand 1860
 
1.7%
australia 1630
 
1.5%
canada 1548
 
1.4%
belgium 1281
 
1.2%
Other values (87) 10549
 
9.8%
2025-08-27T13:26:26.384877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 154156
20.8%
a 109366
14.8%
l 91715
12.4%
d 82067
11.1%
g 63895
8.6%
E 61337
 
8.3%
e 23059
 
3.1%
r 19406
 
2.6%
t 15425
 
2.1%
o 13323
 
1.8%
Other values (39) 105729
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 739478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 154156
20.8%
a 109366
14.8%
l 91715
12.4%
d 82067
11.1%
g 63895
8.6%
E 61337
 
8.3%
e 23059
 
3.1%
r 19406
 
2.6%
t 15425
 
2.1%
o 13323
 
1.8%
Other values (39) 105729
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 739478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 154156
20.8%
a 109366
14.8%
l 91715
12.4%
d 82067
11.1%
g 63895
8.6%
E 61337
 
8.3%
e 23059
 
3.1%
r 19406
 
2.6%
t 15425
 
2.1%
o 13323
 
1.8%
Other values (39) 105729
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 739478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 154156
20.8%
a 109366
14.8%
l 91715
12.4%
d 82067
11.1%
g 63895
8.6%
E 61337
 
8.3%
e 23059
 
3.1%
r 19406
 
2.6%
t 15425
 
2.1%
o 13323
 
1.8%
Other values (39) 105729
14.3%
Distinct2391
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2025-08-27T13:26:26.601016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length29
Median length25
Mean length12.25941
Min length4

Characters and Unicode

Total characters1260439
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique484 ?
Unique (%)0.5%

Sample

1st rowTerry Griffiths
2nd rowTerry Griffiths
3rd rowAlex Higgins
4th rowTerry Griffiths
5th rowAlex Higgins
ValueCountFrequency (%)
mark 5337
 
2.6%
john 3279
 
1.6%
stephen 2840
 
1.4%
david 2597
 
1.3%
paul 2418
 
1.2%
steve 2352
 
1.1%
joe 2289
 
1.1%
michael 2189
 
1.1%
stuart 1706
 
0.8%
jamie 1635
 
0.8%
Other values (2646) 179807
87.1%
2025-08-27T13:26:26.873386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 108576
 
8.6%
a 103910
 
8.2%
103635
 
8.2%
n 98198
 
7.8%
i 79330
 
6.3%
r 76358
 
6.1%
o 67024
 
5.3%
l 59922
 
4.8%
t 48314
 
3.8%
s 39005
 
3.1%
Other values (58) 476167
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1260439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 108576
 
8.6%
a 103910
 
8.2%
103635
 
8.2%
n 98198
 
7.8%
i 79330
 
6.3%
r 76358
 
6.1%
o 67024
 
5.3%
l 59922
 
4.8%
t 48314
 
3.8%
s 39005
 
3.1%
Other values (58) 476167
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1260439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 108576
 
8.6%
a 103910
 
8.2%
103635
 
8.2%
n 98198
 
7.8%
i 79330
 
6.3%
r 76358
 
6.1%
o 67024
 
5.3%
l 59922
 
4.8%
t 48314
 
3.8%
s 39005
 
3.1%
Other values (58) 476167
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1260439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 108576
 
8.6%
a 103910
 
8.2%
103635
 
8.2%
n 98198
 
7.8%
i 79330
 
6.3%
r 76358
 
6.1%
o 67024
 
5.3%
l 59922
 
4.8%
t 48314
 
3.8%
s 39005
 
3.1%
Other values (58) 476167
37.8%
Distinct67
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
2025-08-27T13:26:27.021392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length20
Median length7
Mean length7.2012566
Min length4

Characters and Unicode

Total characters740390
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowWales
2nd rowWales
3rd rowNorthern Ireland
4th rowWales
5th rowNorthern Ireland
ValueCountFrequency (%)
england 61009
56.6%
scotland 9118
 
8.5%
wales 8511
 
7.9%
ireland 7677
 
7.1%
china 4285
 
4.0%
northern 3431
 
3.2%
thailand 1882
 
1.7%
australia 1652
 
1.5%
canada 1332
 
1.2%
belgium 1112
 
1.0%
Other values (66) 7771
 
7.2%
2025-08-27T13:26:27.210311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 154612
20.9%
a 108577
14.7%
l 93477
12.6%
d 83063
11.2%
g 63511
8.6%
E 61104
 
8.3%
e 22784
 
3.1%
r 18785
 
2.5%
t 16324
 
2.2%
o 14953
 
2.0%
Other values (36) 103200
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 740390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 154612
20.9%
a 108577
14.7%
l 93477
12.6%
d 83063
11.2%
g 63511
8.6%
E 61104
 
8.3%
e 22784
 
3.1%
r 18785
 
2.5%
t 16324
 
2.2%
o 14953
 
2.0%
Other values (36) 103200
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 740390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 154612
20.9%
a 108577
14.7%
l 93477
12.6%
d 83063
11.2%
g 63511
8.6%
E 61104
 
8.3%
e 22784
 
3.1%
r 18785
 
2.5%
t 16324
 
2.2%
o 14953
 
2.0%
Other values (36) 103200
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 740390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 154612
20.9%
a 108577
14.7%
l 93477
12.6%
d 83063
11.2%
g 63511
8.6%
E 61104
 
8.3%
e 22784
 
3.1%
r 18785
 
2.5%
t 16324
 
2.2%
o 14953
 
2.0%
Other values (36) 103200
13.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.5 KiB
False
71893 
True
30921 
ValueCountFrequency (%)
False 71893
69.9%
True 30921
30.1%
2025-08-27T13:26:27.269124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

is_international_tournament
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.5 KiB
False
69968 
True
32846 
ValueCountFrequency (%)
False 69968
68.1%
True 32846
31.9%
2025-08-27T13:26:27.300458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Interactions

2025-08-27T13:26:22.004091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.048386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.480540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.769885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.078704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.386873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.701361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:22.048728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.127676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.523300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.814152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.123036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.431660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.745211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:22.086994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.182438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.561643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.852834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.166292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.473097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.786456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:22.129398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.244971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.603639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.894934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.210683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.522116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.829723image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:22.175636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.312393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.646429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.939014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.256128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.568361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.875033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:22.217845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.356467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.689271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.983227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.299798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.612996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.918709image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:22.261458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.402634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:20.731374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.034451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.345508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.658840image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-08-27T13:26:21.963223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-08-27T13:26:27.328815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
best_ofis_international_matchis_international_tournamentmatch_idplayer1_scoreplayer2_scoreseasontournament_countrytournament_idtournament_prize_poolyear
best_of1.0000.1190.203-0.0640.9430.4650.2550.190-0.0410.558-0.439
is_international_match0.1191.0000.1650.2170.0670.0510.2660.2090.2030.0700.251
is_international_tournament0.2030.1651.0000.3010.2200.1330.3901.0000.2740.1730.358
match_id-0.0640.2170.3011.000-0.054-0.0470.8000.2950.982-0.0170.169
player1_score0.9430.0670.220-0.0541.0000.5200.1460.135-0.0310.547-0.397
player2_score0.4650.0510.133-0.0470.5201.0000.0830.082-0.0360.288-0.154
season0.2550.2660.3900.8000.1460.0831.0000.2050.8130.3120.934
tournament_country0.1900.2091.0000.2950.1350.0820.2051.0000.2750.1960.301
tournament_id-0.0410.2030.2740.982-0.031-0.0360.8130.2751.000-0.0030.153
tournament_prize_pool0.5580.0700.173-0.0170.5470.2880.3120.196-0.0031.000-0.035
year-0.4390.2510.3580.169-0.397-0.1540.9340.3010.153-0.0351.000

Missing values

2025-08-27T13:26:22.572371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-27T13:26:22.766472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tournament_idmatch_idstagebest_ofplayer1_nameplayer2_nameplayer1_scoreplayer2_scoreseasonyeartournament_nametournament_countrytournament_prize_pooltournament_statusplayer1_countryplayer2_countrymatch_winnermatch_winner_countryis_international_matchis_international_tournament
075382716Final31Terry GriffithsAlex Higgins16151982-19831982UK ChampionshipEngland47000.0ProfessionalWalesNorthern IrelandTerry GriffithsWalesFalseFalse
175382718Semi-final17Terry GriffithsTony Meo971982-19831982UK ChampionshipEngland47000.0ProfessionalWalesEnglandTerry GriffithsWalesFalseFalse
275382717Semi-final17Alex HigginsRay Reardon961982-19831982UK ChampionshipEngland47000.0ProfessionalNorthern IrelandWalesAlex HigginsNorthern IrelandFalseFalse
375382721Quarter-final17Terry GriffithsSteve Davis961982-19831982UK ChampionshipEngland47000.0ProfessionalWalesEnglandTerry GriffithsWalesFalseFalse
475382719Quarter-final17Alex HigginsJohn Spencer951982-19831982UK ChampionshipEngland47000.0ProfessionalNorthern IrelandEnglandAlex HigginsNorthern IrelandFalseFalse
575382722Quarter-final17Tony MeoJohn Virgo961982-19831982UK ChampionshipEngland47000.0ProfessionalEnglandEnglandTony MeoEnglandFalseFalse
675382720Quarter-final17Ray ReardonJimmy White981982-19831982UK ChampionshipEngland47000.0ProfessionalWalesEnglandRay ReardonWalesFalseFalse
775382728Last 1617Steve DavisPatsy Fagan931982-19831982UK ChampionshipEngland47000.0ProfessionalEnglandIrelandSteve DavisEnglandFalseFalse
875382729Last 1617Terry GriffithsDennis Taylor971982-19831982UK ChampionshipEngland47000.0ProfessionalWalesNorthern IrelandTerry GriffithsWalesFalseFalse
975382723Last 1617Alex HigginsDean Reynolds981982-19831982UK ChampionshipEngland47000.0ProfessionalNorthern IrelandEnglandAlex HigginsNorthern IrelandFalseFalse
tournament_idmatch_idstagebest_ofplayer1_nameplayer2_nameplayer1_scoreplayer2_scoreseasonyeartournament_nametournament_countrytournament_prize_pooltournament_statusplayer1_countryplayer2_countrymatch_winnermatch_winner_countryis_international_matchis_international_tournament
1028043275203172Group 15Jack LisowskiMark Selby302019-20202020Championship LeagueEngland0.0ProfessionalEnglandEnglandJack LisowskiEnglandFalseFalse
1028053275203156Group 15Neil RobertsonRyan Day312019-20202020Championship LeagueEngland0.0ProfessionalAustraliaWalesNeil RobertsonAustraliaTrueFalse
1028063275203158Group 15Neil RobertsonMark Selby322019-20202020Championship LeagueEngland0.0ProfessionalAustraliaEnglandNeil RobertsonAustraliaTrueFalse
1028073275203159Group 15Jimmy RobertsonJack Lisowski322019-20202020Championship LeagueEngland0.0ProfessionalEnglandEnglandJimmy RobertsonEnglandFalseFalse
1028083275203163Group 15Jimmy RobertsonNeil Robertson302019-20202020Championship LeagueEngland0.0ProfessionalEnglandAustraliaJimmy RobertsonEnglandTrueFalse
1028093275203168Group 15Neil RobertsonLuca Brecel312019-20202020Championship LeagueEngland0.0ProfessionalAustraliaBelgiumNeil RobertsonAustraliaTrueFalse
1028103275203174Group 15Neil RobertsonJack Lisowski312019-20202020Championship LeagueEngland0.0ProfessionalAustraliaEnglandNeil RobertsonAustraliaTrueFalse
1028113275203167Group 15Mark SelbyLuca Brecel302019-20202020Championship LeagueEngland0.0ProfessionalEnglandBelgiumMark SelbyEnglandTrueFalse
1028123275203169Group 15Mark SelbyRyan Day322019-20202020Championship LeagueEngland0.0ProfessionalEnglandWalesMark SelbyEnglandFalseFalse
1028133275203175Group 15Mark SelbyJimmy Robertson312019-20202020Championship LeagueEngland0.0ProfessionalEnglandEnglandMark SelbyEnglandFalseFalse